Delivering Personalized Content Based on a Social Graph of Sharing Activity of Users of the Open Web

ABSTRACT

A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails. Building of the social graph can include creating an edge in the social graph that is representative of a particular category type. Based on the social graph, personalized digital content can be selected.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 14/523,818, filed Oct. 24, 2014, issued as U.S. Pat. No.9,098,872 on Aug. 4, 2015, which is a continuation of U.S. patentapplication Ser. No. 14/286,768, filed May 23, 2014, issued as U.S. Pat.No. 8,892,734 on Nov. 18, 2014, which is a continuation of U.S. patentapplication Ser. No. 13/526,288, filed Jun. 18, 2012, issued as U.S.Pat. No. 8,751,621 on Jun. 10, 2014, which claims the benefit of U.S.patent application 61/497,814, filed Jun. 16, 2011. These applicationsare incorporated by reference along with all other references cited inthis application.

BACKGROUND OF THE INVENTION

This invention relates to online networks, and more specifically tobuilding a social graph for a network. The social graph includesconnections or interactions between individuals of the network and canbe used to target content and advertisements to individuals better.

The Internet is a global system of interconnected computer networks.People are spending more of their time on the Internet. Via computersand smartphones, people are surfing the Web, sending e-mail, watchingvideos, reading news, making appointments, shopping, and much more. TheInternet and Web has taken market share from many other communicationsmedia including the telephone, television, radio, magazines, andnewspapers. Consequently, content providers and advertisers want tolearn more about the activities and habits of online users in order tobetter target content, including advertisements.

The activities and connections, including sharing activity, of Internetusers of are important to determine how content for users should bepersonalized. Social networking sites, such as Facebook, LinkedIn, andTwitter are membership communities. To become a member, the socialnetwork site collects information about its members, which may includeinformation such as name, phone number, e-mail address, and often muchmore. After becoming a member, the member can add connections to othermembers, such as their friends specified, for example, by name or e-mailaddress. In these communities, the connections static and form a staticsocial network. Within these communities, the activities of its memberscan be tracked.

Despite the success of static social networking sites, the size of theInternet and Web (which can be referred to as the “open Web”) issignificantly larger. Activities of Web users and how these usersinteract with other users are also important information from which todetermine how to personalize content, services, and advertisements.However, unlike a static social network, users in the open Web areanonymous and do not specify their “friends.” Further, in the open Web,due to privacy concerns, any information gathered should not include anypersonally identified information. It is a very difficult task to obtaina social graph of users of the open Web.

Therefore, there is a need for a technique of building a social graph ofusers of the open Web, including tracking of the sharing activity ofusers. This will improve ad targeting and content personalizationaccording to user connection models based on sharing activity amongusers for online, mobile, and IPTV media.

BRIEF SUMMARY OF THE INVENTION

A social graph is built which includes interactions, sharing activity,and connections between the users of the open Web and can be used toimprove ad targeting and content personalization. Sharing activitybetween two users will affect ads or content that both users will bepresented while surfing the Web. This sharing activity includes sendingof links, sending of videos, sending of files, cutting and pasting ofcontent, sending text messages, and sending of e-mails. Building of thesocial graph can include creating an edge in the social graph that isrepresentative of a particular category type. Based on the social graph,personalized digital content can be selected.

A specific implementation of a technique of building a social graph forthe open Web is a product, ShareGraph™, by RadiumOne. ShareGraph is atrademark of RadiumOne. The RadiumOne Web site, www.radiumone.com,ShareGraph, RadiumOne publications (including user guides, tutorials,videos, and others), and other publications about ShareGraph areincorporated by reference. Compared to a walled social network such asFacebook, ShareGraph is made up of who users share content with on theopen Web, and it represents a more accurate description of who our trueconnections are.

A social graph of the open Web identifies the real, dynamic connectionsbetween users. Content providers and advertisers will be able to locatemore easily consumers who matter, which can lead to improved results inselling services or products.

Although people may list 250 or more people in social network (e.g.,Facebook) as friends, typically the true circle of close personalconnections actually much smaller. The fact is a social network can be abit like a giant address book, a way of keeping our contacts up-to-date.But the truly close—and for marketers, valuable—connections occur amongpeople who are actively sharing their lives with each other.

Sharing is an important indicator of interest in a particular subject ortopic, activity, person. The entire Web has become social, allowing usto share content and experiences across the open Web with the people whomatter most to you. Close personal connections are not simply about“checking in” on social networks. Instead, true connections happen whenpeople share experiences, passions, and opinions on sites that reallymean something to them.

A system tracks and evaluates the sharing that takes place amongconsumers who demonstrate these close social connections. A particularimplementation is by RadiumOne, and is called ShareGraph™.

In an implementation, various sharing activities happening throughoutthe open Web—sharing through sharing buttons, copying content intoe-mail, and others—are captured and contributed to building userShareGraph. ShareGraph models user connections with other users, whereconnections are characterized by strength of connection (based on type,recency, frequency, and directionality of sharing) as well as categoryof connection (based on type and category of content being shared—e.g.,information on cheap flights to New York would contribute to a categorytravel to New York).

Each user is identified through unique but anonymous user ID (e.g.,RadiumOne user identifier (R1 UID)). The RadiumOne user ID is stored ina RadiumOne cookie as well as in RadiumOne Operating Storage of UserModels. When RadiumOne cookie is not available, the RadiumOne user ID isevaluated using device fingerprinting algorithms.

User predictive models and ShareGraph are kept up-to-date inside theRadiumOne Operating Storage of User Models.

As new data for a particular user is available, user models andShareGraph are updated inside RadiumOne Operating Storage of UserModels.

Also, user models are being aged according to proprietary algorithms astime passes.

In an implementation, the system uses the ShareGraph to serve extremelyrelevant ads to consumers. The more often strong connections interact onthe open Web, the more ShareGraph can point us to ads they might want tosee and potentially share.

When consumers share content outside of Facebook, they are always doingit with people they have a lot in common with. In many ways, Facebookessentially represents a giant address book in the cloud. It is on therest of the Web that people can show their true graph of connections bywhom they are sharing content with and who they are really influencedby.

ShareGraph looks at information that consumers share with their realconnections and then uses these insights to serve the most relevant adspossible. Unlike a social graph of Facebook (where users are “connected”to people whom they may not know well), the most significant insightsinto consumers can be obtained by understanding what people share with amuch smaller circle of friends.

There are at least four major areas of differentiation betweenShareGraph and others social graphs that allow the system tosignificantly reduce noise in modeling of users and user connectionsand, thus, provide much more precise targeting of advertisement to usersthan the companies using social graphs.

ShareGraph algorithms are supported by highly scalable underlyingtechnology that aggregates and normalizes data from a variety ofsources, models distinct connections between users, and then targets themost relevant ads in real time.

ShareGraph uses no personally identifiable information (PII) of anykind.

An edge between two nodes in a share-graph—a connection between twousers—represents both context (category) and strength. In fact, for twousers, there could be several edges each representing a differentcategory, and each having its own strength. Defined in such way, ashare-graph is technically speaking a weighted multi-graph. To assessstrength of a connection between two users related to a category, wecollect all available interactions between these users—with context thatbelongs to the category—and apply our algorithms. The algorithms takeinto consideration types of interactions, their frequency, recency,directionality, and so forth.

An example of an interaction would be sharing an article or link throughe-mail. The content of an article will allow us to map this interactionto one or several categories. Then we update the connection betweenthese two users in this category (these categories) with this newinteraction.

(1) ShareGraph™ represents real, actionable connections, connectionswhere users sent or share content or information with each other. Thisnaturally happens within a much tighter, most relevant circle. This isunlike vague, static connections of social graphs resulting in a largegroup of people—most of whom having very little in common.

(2) ShareGraph™ leverages all user activities throughout the entireInternet, as opposed to social graphs that are limited to walled gardenof specific social networks.

(3) A ShareGraph™ edge has interest/commercial intent associated withthe connection. This is derived from the type of content or informationbeing shared. This is unlike social graph static edges devoid of anyinterest or intent.

(4) A ShareGraph™ edge has relative strength of interest/intentassociated with the connection. We score strength of a connectionbetween two users based on type of sharing done between these users,frequency of sharing events, recency, etc. This is unlike social graphstatic edges not differentiated by the strength of connections.

In an implementation, a method includes: receiving first activityinformation for a sender of a first link to at least one recipientcollected by a collection resource at a Web site, where no personallyidentifiable information of the sender is collected in the firstactivity information; storing the first activity information at astorage server; receiving second activity information when a recipientaccesses the first link sent by the sender corresponding to the firstactivity information stored at the storage server, where no personallyidentifiable information of the recipient is collected in the secondactivity information; using at least one processor, attempting toidentify a first node representative of the sender in a social graph;and when a first node representative of the sender in a social graph isnot found and after the receiving second activity, creating a secondnode to represent the sender in the social graph.

The method can further include: attempting to identify a third noderepresentative of the recipient in a social graph; when a third noderepresentative of the recipient in a social graph is not found, creatinga fourth node to represent the recipient in the social graph;determining a category for the first link as a first category type; andin the social graph, creating a first edge between the second and fourthnodes, where the first edge is assigned the first category type and afirst weighting for the first category type.

The method can include: receiving third activity information for thesender of a second link the recipient collected by a collection resourceat a Web site; and receiving fourth activity information when therecipient accesses the second link sent by the sender corresponding tothe third activity information stored at the storage server.

The method can include: determining a category for the second link isthe first category type; and in the social graph, increasing a value ofthe first weighting for the first edge between the second and fourthnodes. The method can include: determining a category for the secondlink is the second category type, different from the first categorytype; and in the social graph, creating a second edge between the secondand fourth nodes, where the second edge is assigned the second categorytype and a second weighting associated with the second category type.

The method can include: in the social graph, reducing a value of thefirst weighting when a time for the fourth activity information is morerecent than a time for the second activity information. The method caninclude: based on the social graph including the second and fourthnodes, making a bid to an ad exchange for an ad associated with at leastone of the second node or fourth node.

The collection resource at a Web site that is used to collect firstactivity information can be sharing widget. The collection resource at aWeb site that is used to collect first activity information can be a URLshortening. The collection resource at a Web site that is used tocollect first activity information can include an instant messengerapplication.

Attempting to identify a first node representative of the sender in asocial graph can include: extracting a user identifier from a cookiereceived with the first activity data; and if a match for the useridentifier is not found in the social graph, performing a probabilisticfingerprinting approach using attributes including at least one ofdevice identifiers; IP addresses; operating systems; browsers types;browser versions; or user navigational, geo-temporal, and behavioralpatterns.

In an implementation, a method includes: collecting activity data fromWeb sources using collection devices, where the activity data does notcontain any personally identifiable information; identifying users andsharing activity between the users in the activity data; using at leastone processor, forming a social graph of the users and sharing activity,where users are represented as nodes in the social graph and sharingactivity are represented as edges in the social graph; and using thesocial graph including sharing activity, selecting an onlineadvertisement for delivery to a user in the social graph.

The collection devices can include URL shortening. The collectiondevices can include an instant messaging application. Each edge betweennodes in the social graph can represent a different sharing category.

The identifying users and sharing activity between the users in theactivity data can include: extracting a user identifier from a cookie inthe activity data; and identifying a first node in the social graphbased on the user identifier. A value of an edge connected to the firstnode can be updated.

In an implementation, a method includes: collecting activity data fromWeb sources using collection devices; based the activity data, forming asocial graph having a first node and first-degree nodes connected to thefirst node, where each first-degree node is connected to second-degreenodes; determining edges between first-degree and second-degree nodesinclude a first category type; determining no edges exist between thefirst node and the first-degree nodes of the first category type; andbased on the edges of the first category type between first-degree andsecond-degree nodes, using at least one processor, making an inferencethat the first node has an interest associated with the first categorytype.

The collection devices can include URL shortening. The collectiondevices can include instant messaging.

Other objects, features, and advantages of the present invention willbecome apparent upon consideration of the following detailed descriptionand the accompanying drawings, in which like reference designationsrepresent like features throughout the figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a simplified block diagram of a client-server system andnetwork in which an embodiment of the invention may be implemented.

FIG. 2 shows a more detailed diagram of an exemplary client or servercomputer which may be used in an implementation of the invention.

FIG. 3 shows a system block diagram of a client or server computersystem used to execute application programs such as a web browser ortools for building a social graph according to the invention.

FIG. 4A shows a system for activity collection and building a socialgraph including sharing activity between users.

FIG. 4B shows some examples of system-owned collection resources.

FIG. 4C shows some examples of partner-owned collection resources.

FIG. 5 shows a social graph with nodes representing users and edgesrepresenting sharing activity between the users.

FIG. 6 shows a more detailed diagram of a system utilizing a socialgraph with sharing activity.

FIG. 7 shows a flow diagram of building a social graph from sharingactivity between a sender and recipient of the open Web.

FIG. 8 shows a flow diagram of building a social graph from sharingactivity of users of the open Web including creating an edgerepresenting a category type

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a simplified block diagram of a distributed computer network100 which embodiment of the present invention can be applied. Computernetwork 100 includes a number of client systems 113, 116, and 119, and aserver system 122 coupled to a communication network 124 via a pluralityof communication links 128. Communication network 124 provides amechanism for allowing the various components of distributed network 100to communicate and exchange information with each other.

Communication network 124 may itself be comprised of many interconnectedcomputer systems and communication links. Communication links 128 may behardwire links, optical links, satellite or other wirelesscommunications links, wave propagation links, or any other mechanismsfor communication of information. Various communication protocols may beused to facilitate communication between the various systems shown inFIG. 1. These communication protocols may include TCP/IP, HTTPprotocols, wireless application protocol (WAP), vendor-specificprotocols, customized protocols, and others. While in one embodiment,communication network 124 is the Internet, in other embodiments,communication network 124 may be any suitable communication networkincluding a local area network (LAN), a wide area network (WAN), awireless network, a intranet, a private network, a public network, aswitched network, and combinations of these, and the like.

Distributed computer network 100 in FIG. 1 is merely illustrative of anembodiment incorporating the present invention and does not limit thescope of the invention as recited in the claims. One of ordinary skillin the art would recognize other variations, modifications, andalternatives. For example, more than one server system 122 may beconnected to communication network 124. As another example, a number ofclient systems 113, 116, and 119 may be coupled to communication network124 via an access provider (not shown) or via some other server system.

Client systems 113, 116, and 119 typically request information from aserver system which provides the information. For this reason, serversystems typically have more computing and storage capacity than clientsystems. However, a particular computer system may act as both as aclient or a server depending on whether the computer system isrequesting or providing information. Additionally, although aspects ofthe invention has been described using a client-server environment, itshould be apparent that the invention may also be embodied in astand-alone computer system.

Server 122 is responsible for receiving information requests from clientsystems 113, 116, and 119, performing processing required to satisfy therequests, and for forwarding the results corresponding to the requestsback to the requesting client system. The processing required to satisfythe request may be performed by server system 122 or may alternativelybe delegated to other servers connected to communication network 124.

According to the teachings of the present invention, client systems 113,116, and 119 enable users to access and query information stored byserver system 122. In a specific embodiment, a “web browser” applicationexecuting on a client system enables users to select, access, retrieve,or query information stored by server system 122. Examples of webbrowsers include the Internet Explorer browser program provided byMicrosoft Corporation, and the Firefox browser provided by Mozilla, andothers.

FIG. 2 shows an exemplary computer system (e.g., client or server) ofthe present invention. In an embodiment, a user interfaces with thesystem through a computer workstation system, such as shown in FIG. 2.FIG. 2 shows a computer system 201 that includes a monitor 203, screen205, enclosure 207 (may also be referred to as a system unit, cabinet,or case), keyboard or other human input device 209, and mouse or otherpointing device 211. Mouse 211 may have one or more buttons such asmouse buttons 213.

Enclosure 207 houses familiar computer components, some of which are notshown, such as a processor, memory, mass storage devices 217, and thelike. Mass storage devices 217 may include mass disk drives, floppydisks, magnetic disks, optical disks, magneto-optical disks, fixeddisks, hard disks, CD-ROMs, recordable CDs, DVDs, recordable DVDs (e.g.,DVD-R, DVD+R, DVD-RW, DVD+RW, HD-DVD, or Blu-ray Disc), flash and othernonvolatile solid-state storage (e.g., USB flash drive),battery-backed-up volatile memory, tape storage, reader, and othersimilar media, and combinations of these.

A computer-implemented or computer-executable version or computerprogram product of the invention may be embodied using, stored on, orassociated with computer-readable medium. A computer-readable medium mayinclude any medium that participates in providing instructions to one ormore processors for execution. Such a medium may take many formsincluding, but not limited to, nonvolatile, volatile, and transmissionmedia. Nonvolatile media includes, for example, flash memory, or opticalor magnetic disks. Volatile media includes static or dynamic memory,such as cache memory or RAM. Transmission media includes coaxial cables,copper wire, fiber optic lines, and wires arranged in a bus.Transmission media can also take the form of electromagnetic, radiofrequency, acoustic, or light waves, such as those generated duringradio wave and infrared data communications.

For example, a binary, machine-executable version, of the software ofthe present invention may be stored or reside in RAM or cache memory, oron mass storage device 217. The source code of the software of thepresent invention may also be stored or reside on mass storage device217 (e.g., hard disk, magnetic disk, tape, or CD-ROM). As a furtherexample, code of the invention may be transmitted via wires, radiowaves, or through a network such as the Internet.

FIG. 3 shows a system block diagram of computer system 201 used toexecute the software of the present invention. As in FIG. 2, computersystem 201 includes monitor 203, keyboard 209, and mass storage devices217. Computer system 501 further includes subsystems such as centralprocessor 302, system memory 304, input/output (I/O) controller 306,display adapter 308, serial or universal serial bus (USB) port 312,network interface 318, and speaker 320. The invention may also be usedwith computer systems with additional or fewer subsystems. For example,a computer system could include more than one processor 302 (i.e., amultiprocessor system) or a system may include a cache memory.

Arrows such as 322 represent the system bus architecture of computersystem 201. However, these arrows are illustrative of anyinterconnection scheme serving to link the subsystems. For example,speaker 320 could be connected to the other subsystems through a port orhave an internal direct connection to central processor 302. Theprocessor may include multiple processors or a multicore processor,which may permit parallel processing of information. Computer system 201shown in FIG. 2 is but an example of a computer system suitable for usewith the present invention. Other configurations of subsystems suitablefor use with the present invention will be readily apparent to one ofordinary skill in the art.

Computer software products may be written in any of various suitableprogramming languages, such as C, C++, C#, Pascal, Fortran, Perl, Matlab(from MathWorks, www.mathworks.com), SAS, SPSS, JavaScript, AJAX, andJava. The computer software product may be an independent applicationwith data input and data display modules. Alternatively, the computersoftware products may be classes that may be instantiated as distributedobjects. The computer software products may also be component softwaresuch as Java Beans (from Sun Microsystems) or Enterprise Java Beans (EJBfrom Sun Microsystems).

An operating system for the system may be one of the Microsoft Windows®family of operating systems (e.g., Windows 95, 98, Me, Windows NT,Windows 2000, Windows XP, Windows XP x64 Edition, Windows Vista, Windows7, Windows 8, Windows CE, Windows Mobile), Linux, HP-UX, UNIX, Sun OS,Solaris, Mac OS X, Apple iOS, Android, Alpha OS, AIX, IRIX32, or IRIX64.Other operating systems may be used. Microsoft Windows is a trademark ofMicrosoft Corporation.

Furthermore, the computer may be connected to a network and mayinterface to other computers using this network. The network may be anintranet, internet, or the Internet, among others. The network may be awired network (e.g., using copper), telephone network, packet network,an optical network (e.g., using optical fiber), or a wireless network,or any combination of these. For example, data and other information maybe passed between the computer and components (or steps) of a system ofthe invention using a wireless network using a protocol such as Wi-Fi(IEEE standards 802.11, 802.11a, 802.11b, 802.11e, 802.11g, 802.11i,802.11n, and 802.11ac, just to name a few examples), near fieldcommunication (NFC), radio-frequency identification (RFID), mobile orcellular wireless (e.g., 2G, 3G, 4G, 3GPP LTE, WiMAX, LTE, Flash-OFDM,HIPERMAN, iBurst, EDGE Evolution, UMTS, UMTS-TDD, 1xRDD, and EV-DO). Forexample, signals from a computer may be transferred, at least in part,wirelessly to components or other computers.

In an embodiment, with a web browser executing on a computer workstationsystem, a user accesses a system on the World Wide Web (WWW) through anetwork such as the Internet. The web browser is used to download webpages or other content in various formats including HTML, XML, text,PDF, and postscript, and may be used to upload information to otherparts of the system. The web browser may use uniform resourceidentifiers (URLs) to identify resources on the web and hypertexttransfer protocol (HTTP) in transferring files on the web.

FIG. 4A shows a system for activity collection and building a socialgraph (e.g., ShareGraph™) for a network of users. The system monitorsusers of the Internet or Web as they surf the Web (e.g., reading news,searching for information, shopping, and so forth). This monitoringoccurs without regard to whether the users are logged into a membershipsite (e.g., Facebook, pay sites, and others), which may be referred toas the open Web 401.

Monitoring occurs at open Web sites by using a system-owned collectionresource 405 or a partner-owned collection resource 407, or both. FIG.4B shows some examples of system-owned collection resources. FIG. 4Cshows some examples of partner-owned collection resources.

The collection resources or collection devices include (1) activitycollection widgets, either system owned 406A or partner 408A, or both;(2) URL shorteners, either system owned 406B or partner 408B, or both(both UI or API, or both); (3) applications or sites where users postand share content, either system owned 406C or partner 408C, or both,(e.g., text, images, audio, or video, or any combination of these); and(4) communication applications, either system owned 406D or partner408D, or both (e.g., instant messenger, text chat, voice chat, videochat, or others).

Resources 405 and 407 will gather activity data and pass this data to anactivity storage server 412, typically via the Internet. Partnerresource 407 may be processed by a partner back end, and then this datais passed to activity storage server 412.

For example, referring to FIG. 4C, partner activity collect widget 408Ais passed to partner back end processing 410A. Partner URL shortener408B is passed to partner back end processing 410B. Partner activitycollection application 408C is passed to partner back end processing410C. Partner activity collection messenger 408D is passed to partnerback end processing 410D. From the back end processing blocks 410A,410B, 410C, and 410D, data is passed to activity storage server 412.

Collection widget. In an implementation, a collection widget is writtenin JavaScript code. A widget can also be written in other languages.This widget is added at various Web sites to gather activity data aboutusers' interactions at that site. When a user visits a particular Website with the widget, the widget executes via a Web browser of thevisiting user. The user may not be aware that the widget is collectingactivity data. However, in an implementation, the activity data gathereddoes not include any personally identifiable information.

Widget 406A is usually controlled or operated by the same entity that isrunning activity server 412. Typically, widget 406A is added to orpresented at a third party site. The widget owner may pay the thirdparty site operator for adding the widget to the third party's Web site.

A user at third party site can use the widget to share something on thesite. For example, the user can share an article or link with theirfriends, such as posting on their Facebook or other social networkingpage, posting on their Twitter or other microblogging site, e-mail toothers, send via a messenger program, or other sharing activity. Widget406A will gather this activity data and pass this data to an activitystorage server 412, typically via the Internet.

Partner widget 408A is operated by an entity different from the entityrunning activity server 412. Widget 408A will gather activity data andpass this data, typically via the Internet, for back end processing 410Acontrolled by the partner entity (e.g., back end processing 410A mayinclude a partner's activity storage server). Back end processing server410A will then pass data to activity storage server 412. This can bedone via, for example, the Internet (e.g., server-to-server API call) orvia secure FTP (SFTP) batch transfer. The data can also be passed via aWeb browser call.

Owner of widget 408A or 410A typically enters a business relationship orarrangement with an entity running a Web site to have the widgetinstalled at a site. Once installed, the widget will collect activitydata from the site. In short, widgets 406A and 408A will gather sharingactivity data and pass this data to activity storage server 412,typically via the Internet. Information collected via partner widget408A may be processed by a back end process server 410A and thenforwarded to activity server 412.

A system can include either widget 406A or widget 408A types. Thesewidgets collect activity information from third party sites. A user(sender) of a third party site uses the widget to share an article orother information available at the site. The sender clicks on the widgetand sends a link to another user (recipient). The widget captures thelink, but does not look at any personally identifier or privateinformation such as the contents of an e-mail or message. When therecipient clicks on the link, a sharing connection is created betweenthe recipient and the sender.

URL shortener. An activity collection resource can be a uniform resourcelocator (URL) shortening tool or application. URL shortening is atechnique on the Web in which a URL may be made substantially shorter inlength and still direct to the required page. This is achieved by usingan HTTP redirect on a domain name that is short, which links to the webpage that has a long URL. Some examples of URL shortening include sitesby TinyURL, Bitly Enterprise, and re.Po.st Enterprise by RadiumOne.

The URL shortening site can be system owned 406B or partner owned 408B.Similar to widgets 406A and 408A, activity collected via a shorteningsite is sent to activity server 412. When partner owned, activity datafrom site 408B may be processed by back end 410B before sending toactivity server 412.

To create a shortened URL, a user enters the full URL at the shorteningsite. The site presents the user with the shortened URL which the usercan send or post. When the recipient of the shortened URL clicks on it,the recipient is redirected to the full site.

The shortened URL created is a unique link. Once the recipient clicks onthe link, this can identify a sharing connection between the sender ofthe link and the recipient. This activity data is captured by thesystem.

Social media sharing application or site. Applications (e.g., a mobileapp on a smartphone or tablet computer) or sites allow sharing ofinformation with others. These can be used to collect activity data.They can be system owned applications 406C or partner owned 408C. Someexamples of such sites include Facebook (e.g., Instagram), Pinterest,Tumblr, and via.me. With a social media sharing application or site,users can publish links and other information for others.

A user (sender) can share information (e.g., video, photo, link,article, or other) by posting to a site. The user can post directly onthe site or use a application program, such as a mobile application on asmartphone or tablet computer. When another user (recipient) clicks orvies the link, there is connection activity between the sender andrecipient. This activity data is captured by the system.

Messenger. Applications (e.g., a mobile app on a smartphone or tabletcomputer) or sites allow Internet or Web messaging with others. Internetmessaging is different from short messaging server (SMS) or textmessaging. Messenger applications can be used to collect sharingactivity data. An example of a messenger application site is pingme.net,which is a cross-platform messaging application, and WhatsApp messenger.These applications can also do group messaging.

Users use messenger application to send links and other information toother users. A user (sender) can copy link (e.g., via a clipboard) andsend to one or more users via the messenger application. When arecipient user clicks on the link, there is connection activity betweenthe sender and recipient for that link.

Sharing activity data can be captured as described above. There can bedifferent data collectors for different devices and platforms. Forexamples, there can be different applications for Android Web, Androidapplication, and desktop Web.

The activity data is transmitted to and stored at activity storageserver 412, typically through the Internet. Server 412 stores the datafor further processing. There can be a significant amount of real-timedata that is collected for processing. The file and processing systemmay employ infrastructure and databases such as Oracle, IBM, or Apache™Hadoop™. Distributed computing and processing can be used to process thedata.

In an implementation, the Apache Hadoop software library is a frameworkthat allows for the distributed processing of large data sets acrossclusters of computers using a simple programming model. It is designedto scale up from single servers to thousands of machines, each offeringlocal computation and storage. Rather than rely on hardware to deliverhigh-availability, the library itself is designed to detect and handlefailures at the application layer, so delivering a highly-availableservice on top of a cluster of computers, each of which may be prone tofailures. See hadoop.apache.org for more information.

The activity data collected by the widgets is stored at server 412,usually in a database or file systems (such as Hadoop Distributed FileSystem (HDFS)) on hard drives of the server. There may be many terabytesof data that need are to be processed. Taking the stored activity dataas input is a build-update graph component 421 (e.g., executable coderunning on one or more a servers or other computers). Build-update graph421 can run on the same server that stores the activity data, or may runon a separate server that accesses the storage server 412.

Build-update graph 421 builds or updates a social graph using thecollected activity data. The social graph can be stored in one or moredatabases or file systems (such as Hadoop file system). Build-updategraph 421 includes three components: (1) an identify nodes and edges forsocial graph that need to be updated 423, (2) a create new nodes/edgesif nodes/edges are not found 426, and (3) an update values associatedwith nodes and edges 428.

For the incoming activity data collected, identify nodes 423 scansthrough and finds the nodes and edges of the social graph that need tobe updated. As discussed above, no personally identifiable information(PII) is collected in the activity data.

Some examples of personally identifiable information includes, forexample, name, such as full name, maiden name, mother's maiden name, oralias; address information, such as street address or e-mail address;mobile telephone; and credit card information. Personally identifiableinformation is information that can be used to uniquely identify,contact, or locate a single person or can be used with other sources touniquely identify a single individual.

In contrast to personally identifiable information, nonpersonalinformation is information that is recorded about users so that it nolonger reflects or references an individually identifiable user. Someexamples of nonpersonal information include cookies, Web beacons,demographic information (e.g., zip code, age, gender, and interests),server log files, environmental variables (e.g., operating system, OSversion, Internet browser, and screen resolution), and others.

The incoming activity gathered by resources 405 and 407 have nopersonally identifiable information. Similarly, the nodes of the socialgraph have profiles with no personally identifiable information, beinganonymous and simply identified.

Using nonpersonal information in the activity data, identify nodes 423attempts to find a corresponding or matching node (e.g., anonymous userprofiles) or edges, or both, in the nonpersonal information contained inthe social graph. A matching algorithm can use a variety of factors tomatch nodes (or users).

User identification and device fingerprinting. In a specificimplementation, the RadiumOne system identifies a user uniquely butanonymously by RadiumOne user ID. This can be a randomly generated butunique alphanumerical string that is assigned to a user or node when thesystem first time processes this user's activity or event (such asviewing a webpage, sharing a link, or other activity).

This user ID is then stored in the User Identification AttributesStorage of the RadiumOne Operating Storage of User Models. UserIdentification Attributes Storage also stores various devicefingerprinting attributes associated with this user ID: various deviceIDs and their combinations and hashes, IP addresses, operating systemsand browsers (e.g. type and version via a user agent field); typicaluser navigational, geo-temporal, and behavioral patterns, variouspersonalized elements in the Web pages often visited by the user,various personalized elements of user device or browser configuration,such as browser window size, and others.

When a user uses a browser that supports cookies, a user ID is alsostored in a RadiumOne browser cookie.

When the RadiumOne system is processing a user activity data (such asviewing a Web page, sharing a link, or other activity), it is trying toattribute this activity to one or several user IDs from the UserIdentification Attributes Storage as well as probabilities that thisactivity is performed by these user IDs (as described below). If no userID from the User Identification Attributes Storage could be attributedto the activity with high enough probability (above certain threshold),a new user ID is generated by the system and:

(1) This user ID is assigned to this activity with probability 1.

(2) This user ID is stored in the User Identification AttributesStorage, together with the device fingerprinting attributes that wereavailable in this user activity data.

(3) When a user uses a browser that supports cookies, a user ID is alsostored in a RadiumOne browser cookie.

When the RadiumOne system is trying to attribute this activity to one orseveral user IDs from the User Identification Attributes Storage, aswell as evaluate probabilities that this activity is performed by theseuser IDs, it first checks if a RadiumOne cookie with RadiumOne user IDis available in the received user activity data.

(1) If it is, the RadiumOne system extracts user ID from the cookie.This user ID is then attributed this user ID to this activity. Theprobability of the match is set to 1.

(2) If it is not, the RadiumOne system extracts all devicefingerprinting attributes that are available in the received useractivity data. The RadiumOne system then applies RadiumOne probabilisticfingerprinting algorithms to these attributes and calculates matchprobabilities—probabilities that this set of device fingerprintingattributes belongs to user IDs from the User Identification AttributesStorage. User IDs from the User Identification Attributes Storage withthe highest match probabilities—the match probabilities above certainthresholds—and associated match probabilities are then attributed tothis activity.

When a node or edge is found (430), update values 428 will update thenode or an edge (e.g., associated with the node). When a node or edge isnot found (431), then create new node or edge 426 is created a new nodeor edge in the graph. The result of build/update graph 421 is a socialgraph 434 with nodes modeling user profiles and edge modeling sharingactivities among users.

FIG. 5 shows a sample social graph 501 where circles 503 represent nodesand lines are edges 506 representing sharing interactions between nodes.There can be one or more edges 506 between two nodes. Several edgesbetween nodes typically indicate sharing activities along severalcategories: e.g., travel, computers, sports, and others.

Nodes connected together directly have one degree of separation. Nodesconnected through one other node have two degrees of separation.Depending on a number of intervening nodes between two nodes, this willbe a number of degrees of separation between the two nodes. For example,node 518 is one degree of separation away from node 503. Node 515 is twodegrees of separation from node 503.

In a specific implementation, edges between nodes indicate sharingactivities along several categories such as travel, computers, sports,and so forth. For each additional new sharing category, an additionaledge is added. In a specific implementation, for each additional newsharing interest category, an additional edge is added. Further, in animplementation, the sharing interaction or edges between the nodes isweighted (e.g., weighting in a range from 0 to 1), so that certain typesof sharing interactions are given different significance in the system.Weight can be used to represent a relative strength of interactionrelated to a particular interest category.

Some types of sharing activities that are tracked for the social graph(or share graph) include: sending messages between users; sending filesbetween users; sending videos between users; sending an e-mail (e.g.,Web e-mail) with a link from one user to another such as sharing a linkto various social media sites like Facebook or Twitter; and sendinginstant messages between users. For mobile users on smart phones, thesharing activities can further include: sending SMS-type messagesbetween users.

As discussed above, the system does not look at personal information, sothe private information in the message or e-mail is not collected. Butthe words and links (e.g., link, short cut, or URL) from a Web site thatthe users copies such as by using a using a cut (or copy) operation andpaste operation to send to another user can be part of the activity datacollected by the collection resource 405 or 407. The copied link andinformation at the link is not personal information. From theinformation at the link, the system can determine the category ofinformation that is being shared. The system can include automated textclassification to classify articles and information at links.

Once two users connect, such as a user 515 (represented by a node 515 inFIG. 5) sends user 518 (represented by node 518) a message containing alink concerning travel (which was collected by resource 405 or 407).When recipient user 518 clicks on the link from sender user 515, thesystem will add an edge to the graph to represent the activity. An edge520 is added to the graph to represent this sharing activity between thetwo users. Edge 520 is assigned a particular weight. A new “travel” edgewill be added between these two users if it did not exist before. If itdid, its weight will be increased.

In a specific implementation, two users are connected when one user(sender) shares information with another user or group and the otheruser (recipient) consumes the information that was sent (e.g.,clicked-back on the shared link, opened an attachment, opened amessage). For example, simply placing a link on Facebook wall so thatall 250 Facebook “friends” can see this link or tweeting a link to 250Twitter followers will not create a connection between the sender, orsharer, and 250 people in the graph. This would create significant noisein the system. The connections are created between the sender and onlythose users who clicked back on (or otherwise consumed) the message.

For example, more recently sent messages are given a greater weight thanolder messages. So edge 520 may be assigned a particular weight, whileone or more other edges between nodes 515 and 518 are reduced by someamount. As time passes, the contribution of individual sharingactivities to a particular edge will decrease. For example, the morerecently sent messages are given a greater weight than older messages.In an implementation, the system graph tracks time, and updates theweights of edges (e.g., reduces their value) when updating other edgesbetween two nodes. Further, there can a periodically purge (e.g.,monthly) of stale nodes and edges. For example, nodes and edges thathave been inactive more than a threshold amount of time or nodes andedges with weight (strength) falling below a certain threshold can bepurged.

By tracking sharing activity between users instead of just solo users,this will allow better understanding of the characteristics of 518. Forexample, during user 518 surfing, user 518 has never before indicated aninterest in travel. However, by recording the sharing interaction,because user 515 sent some travel related content to user 518 (and user518 consumed this content), the system can attribute or imply aninterest in travel to user 518, even though user 518 has never had anypersonal surfing to indicate this. Therefore, using the ShareGraphsocial graph, which tracks sharing activity, more and better informationabout the users can be gathered.

The system can handle users it does not have much information. Forexamples, user interest in a particular category depends on what wasshared. For example, nodes connected to the person are interested intravel, but they did not share travel with this person yet. And thisperson has not navigated travel sites (no preexisting behavior). Thesystem can infer the person has an interest in travel, but with lowerconfidence level.

Further depending on how user 518 interacted with the message from user515, strength of edge 520 can be increased or decreased. For example, ifuser 518 clicked on the link and purchased something on the link. Thisindicated a very strong interest, and an edge 520 can be updated with anappropriate value. If user 518 discarded the message without clicking onthe link then edge 510 will be update to indicate a low strength.

The strength of an edge is affected by: (1) user A (message recipient)reads the message; (2) user A reads and re-sends the message to user B;(3) user B does not read message from user A; and (4) user B reads themessage from user A. Generally, action 4 is stronger than action 3,which is stronger than 2, which is stronger than 1.

The strength or weight of an edge also depends on how many users themessage was sent to. In general, the smaller the group of therecipients, the more the strength will increase if the user (a messagerecipient) clicks back, but the more strength will decrease if the user(a message recipient) does not click back. For a group message to 24people versus a direct message to one person, when there are 24potential recipients, each click back will increase weight less than ifthe only one direct recipient clicks back.

Collection devices collect sharing activity. The social graph continuesto be updated with the new activity data and the passage of time. Thesocial graph is continuously updated by new nodes being added, new edgesbeing added, nodes being updated, edges being updated, strengths andweightings being updated due to new activity, and the graph is updateddue to the passage of time because more recent information is treated asbeing more important than past activity data. Generally, strengthdecreases as time passes (recency decreases) and increases when severalsharing activities happened during a relatively short period of time(frequency).

A traditional social graph may have some vague relationship betweenusers, where an edge of the graph does not tell how strong or real theconnection is and what kind of interest or commercial intent the usersare sharing. So ads delivered to these users may not be particularrelevant.

In the ShareGraph social graph, there is some action between users.There is a real connection of influencer and influence. ShareGraphtracks kind of interest or intent the users are sharing. For example,when user A sends user B an article on Android phones or link to acoupon for www.elitetoystore.com, this means there is a shared interestor intent is Android or toys. This is much more valuable information,especially for ad targeting.

In a specific implementation, the strength algorithms have a hierarchyfor interest categories or granularity. For example, an Android phoneinterest will increase the strengths of both an Android edge and amobile phone edge. The Android edge strength increase will be greaterthan the mobile phone edge.

Also, weighting of an edge depends on how accurate and relevant a sharedlink or content categorization is. Sharing a review article on thelatest Android OS (e.g., Android Ice Cream Sandwich) has higherconfidence level of Android interest than sharing an article on latestconsumer electronics show (CES) that mentions the latest Android.

In a specific implementation, the shared social graph for the open Webtechnology ports data from a variety of sources creates distinctconnections between users, and then targets the necessary ads behind thescenes in real time. ShareGraph uses underlying behavior of “sharing” toestablish distinct connections.

The system can match the same user which connects via several devicessuch as a mobile browser, mobile app, regular Web browser. The systemproviders or makes use of a variety of data collectors or resourcesexisting on different devices and platforms.

In order to build ShareGraph around RadiumOne user ID, various browserand device IDs captured by RadiumOne for users of RadiumOne system ownedas well as third party data collectors should be matched to RadiumOneuser ID for the same user. This is done by using two approaches:

1. Using deterministic match by leveraging system owned and third partypartner applications that have regular (nonmobile) Web, mobile Web, andmobile app presence and require user login. Two RadiumOne cookie IDs(e.g., a mobile one and a regular (nonmobile) Web one), as well asmobile device ID are matched via a common account ID when the same userlogs in into the application via regular (nonmobile Web), via mobileWeb, and via mobile application.

2. Using probabilistic match by leveraging various device connectionalgorithms. These are based on user geo-temporal and behavioralpatterns. Such as matching regular (nonmobile) and mobile Web browserIDs as well as mobile device IDs based on a history of all of themappearing within the same geo-vicinity and the same time interval. Aconfidence level is associated with such matches.

In a specific implementation, the system builds user predictive modelsbased on user various online activities, in particular sharingactivities that are modeled through a ShareGraph.

The process includes two activities:

1. Building and maintaining user predictive models, includingShareGraph. The system builds user predictive models based on uservarious online activities, in particular sharing activities. The data isbased on various data sources including both internal (own) and external(partner data).

2. Serving targeted ads to users based on user predictive models,including ShareGraph.

FIG. 6 shows a more detailed diagram of a system utilizing a socialgraph with sharing activity. The system includes an ad serving engine603, ad bidding engine 605, and content personalization engine 607.

The ad serving engine receives ad serving requests. The ad biddingengine receives ad bidding requests. The content personalization enginereceives content test.

The request engines 603, 605, and 607 are input to a decision makingengine 611, which receives the request for a best fitting ad, bid, orcontent. The decision making engine requests a users' social graph 614,which is built as described above and in FIG. 4. The system selects andupdates the user's social graph 616. Based on the social graph, decisionmaking engine 618 receives the social graph and selects the best fittingad, bid, or content. Engine 618 sends this ad to the ad serving engine623, ad bidding engine 625, or content personalization engine 627 asappropriate.

The system can buy inventories from the system's own publisher networkas well as on real-time bidding (RTB) exchanges. Any time the serverreceives an ad request from a publisher or bid request from an RTBexchange, the server has to decide on what ad to serve to thisparticular user. The time allowed to respond to a bid request may be,for example, 120 milliseconds. So, the decision and exchange occurs in areal time.

In an implementation, in a first step, the RadiumOne server obtains aRadiumOne user ID. For an ad request from a publisher network, theRadiumOne user ID is obtained from RadiumOne cookie. For a bid requestfrom an RTB exchange, RTB exchange sends exchange user ID along with abid request which is then converted into the RadiumOne user ID. TheRadiumOne user IDs are matched to exchange user IDs through a cookiematching process.

Once the RadiumOne user ID is obtained, a user predictive modelcorresponding to this RadiumOne user ID is retrieved from RadiumOneOperating Storage of User Models and is used for choosing the mosttargeted ad, bid, or content for the given ad, bid, or content servingopportunity.

FIG. 7 shows a flow 705 for building a social graph from sharingactivity between a sender and recipient of the open Web.

In a step 710, first activity information is received for a sender of afirst link to at least one recipient. The activity information iscollected by a collection resource at a Web site. None of the sender'spersonally identifiable information is collected when collecting thefirst activity information.

In a step 715, the first activity information is stored at a storageserver.

In a step 720, second activity information is received when a recipientaccesses the first link sent by the sender. This corresponds to thefirst activity information stored at the storage server. None of therecipient's personally identifiable information is collected whencollecting the second activity information.

In a step 725, there is an attempt to identify a first noderepresentative of the sender in a social graph.

In a step 730, when a first node representative of the sender in asocial graph is not identified, a second node is created to representthe sender in the social graph.

FIG. 8 shows a flow 805 of building a social graph from sharing activityof users of the open Web including creating an edge representing acategory type.

In a step 810, first activity information is received for a sender of afirst link to at least one recipient. The activity information iscollected by a collection resource at a Web site. None of the sender'spersonally identifiable information is collected when collecting thefirst activity information.

In a step 815, the first activity information is stored at a storageserver.

In a step 820, second activity information is received when a recipientaccesses the first link sent by the sender. This corresponds to thefirst activity information stored at the storage server. None of therecipient's personally identifiable information is collected whencollecting the second activity information.

In a step 826, using the first activity information, a first node in asocial graph is identified as being representative of the sender.

In a step 829, using the second activity information, a second node in asocial graph is identified as being representative of the recipient.

In a step 832, a first category type is determined as a category for thefirst link. The first link is associated with the first activityinformation (e.g., sender of first link to at least one recipient) andsecond activity information (e.g., recipient accesses the first link).

In a step 836, a first edge is created between first and second nodes inthe social graph. The first edge is representative of the first categorytype.

This description of the invention has been presented for the purposes ofillustration and description. It is not intended to be exhaustive or tolimit the invention to the precise form described, and manymodifications and variations are possible in light of the teachingabove. The embodiments were chosen and described in order to bestexplain the principles of the invention and its practical applications.This description will enable others skilled in the art to best utilizeand practice the invention in various embodiments and with variousmodifications as are suited to a particular use. The scope of theinvention is defined by the following claims.

The invention claimed is:
 1. A method comprising: receiving firstactivity information for a sender of a first link to at least onerecipient collected by a collection resource at a Web site, wherein nopersonally identifiable information of the sender is collected incollecting the first activity information; storing the first activityinformation at a storage server; receiving second activity informationwhen a recipient accesses the first link sent by the sendercorresponding to the first activity information stored at the storageserver, wherein no personally identifiable information of the recipientcollected in collecting the second activity information; using at leastone processor, attempting to identify a first node representative of thesender in a social graph; when a first node representative of the senderin a social graph is not identified and after receiving the secondactivity information, creating a second node to represent the sender inthe social graph; and based on at least information associated with thesecond node in the social graph, selecting a personalized digitalcontent for delivery to the sender.
 2. The method of claim 1 furthercomprising: maintaining the social graph comprising: using activityinformation stored at the storage server, determining a plurality ofnodes representing persons specified in a plurality of activityinformation stored at the storage server; generating a plurality ofedges in the social graph, wherein each edge represents at least oneactivity information of the plurality of activity information; and usingthe plurality of edges to couple the plurality of nodes.
 3. The methodof claim 2 wherein the determining the plurality of nodes representingpersons specified in a plurality of activity information stored at thestorage server comprises disallowing access to personally identifiableinformation of the persons.
 4. The method of claim 1 wherein the socialgraph comprises a plurality of activity information, each activityinformation represented in the social graph by an edge coupling at twonodes of the social graph.
 5. The method of claim 1 wherein the firstactivity information is generated by a mobile application.
 6. The methodof claim 1 wherein the first activity information is generated byactivity at a Web site.
 7. The method of claim 1 wherein the selecting apersonalized digital content for delivery to the sender is in responseto at least one of a bid request from a real-time bidding exchange or anad request from a publisher network.
 8. The method of claim 1 wherein atthe plurality of activity data comprises at least one activity datacollected from a sharing widget installed on a device of the sender. 9.The method of claim 1 wherein the plurality of activity data comprisesactivity data collected from an instant messaging application.
 10. Themethod of claim 1 wherein selecting a personalized digital content fordelivery to the sender comprises: using a user predictive modelappropriate for the second node in the social graph to select thepersonalized digital content, wherein the user predictive modelconsiders sharing activity associated with the second node
 11. Themethod of claim 1 wherein the first activity information comprises atleast one of sending a video, sending a file, cutting and pasting ofcontent, or sending of e-mail.
 12. The method of claim 1 wherein thefirst activity information comprises sending an instant message from thesender to the recipient.
 13. The method of claim 1 wherein each edgebetween nodes in the social graph represents a sharing category.
 14. Themethod of claim 1 comprising: sending a bid in response to a bid requestfrom a real-time bidding exchange, wherein the bid comprises theselected personalized digital content.
 15. The method of claim 1comprising: making a bid to an ad exchange for an advertisement, whereinthe bid comprises the selected personalized digital content.
 16. Themethod of claim 1 wherein the receiving first activity information forthe sender of the first link comprises: sending of an e-mail includingthe first link by the sender via a mobile device.
 17. The method ofclaim 16 wherein the receiving second activity information when therecipient accesses the first link comprises: receiving of the e-mailincluding the first link by the recipient via a mobile device.
 18. Themethod of claim 16 wherein the receiving second activity informationwhen the recipient accesses the first link comprises: receiving of thee-mail including the first link by the recipient via a nonmobile device.19. The method of claim 1 wherein the receiving first activityinformation for the sender comprises: sending of an e-mail including thefirst link by the sender via a nonmobile device.
 20. The method of claim19 wherein the receiving second activity information when the recipientaccesses the first link comprises: receiving of the e-mail including thefirst link by the recipient via a mobile device.
 21. The method of claim19 wherein the receiving second activity information when the recipientaccesses the first link comprises: receiving of the e-mail including thefirst link by the recipient via a nonmobile device.
 22. A methodcomprising: receiving first activity information for a sender of a firstlink to at least one recipient collected by a collection resource at aWeb site, wherein no personally identifiable information of the senderis collected in collecting the first activity information; storing thefirst activity information at a storage server; receiving secondactivity information when a recipient accesses the first link sent bythe sender corresponding to the first activity information stored at thestorage server, wherein no personally identifiable information of therecipient collected in collecting the second activity information; usingat least one processor, attempting to identify a first noderepresentative of the sender in a social graph; when a first noderepresentative of the sender in a social graph is not identified andafter receiving the second activity information, creating a second nodeto represent the sender in the social graph; receiving a bid requestfrom a real-time bidding exchange; determining the bid request isassociated with the second node in the social graph; and based on atleast information associated with the second node in the social graph,determining a first personalized digital content for a bid in responseto the bid request.
 23. The method of claim 22 comprising: using thesocial graph, selecting a second personalized digital content fordelivery to the recipient.
 24. The method of claim 23 wherein the firstand second personalized digital content are different.
 25. The method ofclaim 22 wherein determining a first personalized digital content for abid in response to the bid request comprises: using a user predictivemodel appropriate for the second node in the social graph to select thefirst personalized digital content, wherein the user predictive modelconsiders sharing activity of the second node.
 26. The method of claim22 wherein the bid comprises a bid value that is associated with thefirst personalized digital content.
 27. The method of claim 22 wherein aresponse time for the bid in response to the bid request is about 120milliseconds or less.
 28. The method of claim 22 wherein the bid inresponse to the bid request is for serving of an ad to the sender. 29.The method of claim 22 wherein the attempting to identify a first noderepresentative of the sender in a social graph comprises: extracting afirst person identifier from a cookie in the first activity data; andattempting to match to the first person identifier to a second personidentifier associated with a node in the social graph.
 30. The method ofclaim 22 wherein the determining the bid request is associated with thesecond node in the social graph comprises: receiving a first identifierfrom the real-time bidding exchange; and matching the first identifierto a second identifier associated with the second node in the socialgraph.